Outlier detection and removal using IQR | Feature engineering tutorial python # 4

IQR is another technique that one can use to detect and remove outliers. The formula for IQR is very simple. IQR = Q3-Q1. Where Q3 is 75th percentile and Q1 is 25th percentile. Once you have IQR you can find upper and lower limit by removing this formula, lower_limit = Q1-1.5*IQR upper_limit = Q3 +1.5*IQR Anything less than a lower limit or above the upper limit is considered outlier. We will use python pandas to remove outliers on a sample dataset and in the end, as usual, I have an interesting exercise for you to practice Code & Exercise: https://github.com/codebasics/py/blob/master/ML/FeatureEngineering/3_outlier_IQR/3_outliers_iqr.ipynb Link for kaggle dataset: https://www.kaggle.com/mustafaali96/weight-height Topics 00:00 What is percentile and IQR 04:15 Remove outliers using IQR 06:55 Exercise Do you want to learn technology from me? Check https://codebasics.io/ for my affordable video courses. Website: https://codebasics.io/ Facebook: https://www.facebook.com/codebasicshub Twitter: https://twitter.com/codebasicshub